Interactive course building system with generation assistance and method

Abstract

An interactive course building system with generation assistance is provided for building interactive courses with high efficiency using a generation assistant to conceptualize course goals and generate course content and lessons. The interactive course building system with generation assistance may include a content repository, generation assistant, course curriculum development component, content generation component, lesson generation component, user interface module and interactive elements. A method for building interactive courses with high efficiency using a generation assistant to conceptualize course goals and generate course content and lessons using the interactive course building system with generation assistance is also provided.

Description

INTERACTIVE COURSE BUILDING SYSTEM WITH GENERATION ASSISTANCEAND METHODCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the priority from U.S. provisional patent application serial number 63 / 644,035 filed May 8, 2024, which is incorporated in its entirety herein by reference.FIELD OF THE INVENTION

[0002] The disclosure relates to an interactive course building system with generation assistance. Furthermore, the disclosure relates to building interactive courses with high efficiency using a generation assistant to conceptualize course goals and generate course content and lessons.BACKGROUND

[0003] Creating dynamic quizzes, simulations, and interactive assessments is a challenging and time-consuming task for course developers due to the high level of expertise, creativity, and technical skills needed to design engaging and effective learning activities. Moreover, it often requires a high input of manual work to extensively research a diverse set of topics, sort through irrelevant or outdated content, obtain various permissions for usage of available content, synthesize and organize the content, and regularly review and update the information based on new findings or changes in the content. Additionally, users are required to write questions, create feedback, set parameters, test scenarios, and revise content — all of which is time consuming.

[0004] Thus, a need exists to solve these deficiencies. What is needed is a course building system with a generation assistant to substantially reduce the time requirement to assemble and create high quality course content. What is needed is a system and method for interactive course development using a generation assistant to conceptualize the course objectives, develop course content, and structure course lessons for learners. What is needed is an interactive course generation and administration system to adapt courses presented to a learner to include customized lessons tailed to resolve knowledge gaps for the learner. What is needed is a course building system to supplement course information provided by a user with externally sourced information.SUMMARY

[0005] An aspect of the disclosure advantageously provides a course building system with a generation assistant to substantially reduce the time requirement to assemble and create high quality course content. An aspect of the disclosure advantageously provides a system and method for interactive course development using a generation assistant to conceptualize the course objectives, develop course content, and structure course lessons for learners. An aspect of the disclosure advantageously provides an interactive course generation and administration system to adapt courses presented to a learner to include customized lessons tailed to resolve knowledge gaps for the learner. An aspect of the disclosure advantageously provides a course building system to supplement course information provided by a user with externally sourced information.

[0006] Accordingly, the disclosure may enable a system for conceptualizing and generating an interactive learning course comprising a content repository, generation assistant, course curriculum development component, content generation component, lesson generation component, and user interface. The content repository may store and organize course information accessible to generate course content. The generation assistant may interactively interface with a user via conversational dialog by generating prompts to operate an artificial intelligence (Al) engine. The course curriculum development component may receive course objectives indicated by the user, selectively interrogate the user via the generation assistant to refine the course objectives and reach a consensus of course parameters. The content generation component may generate the course content of the course curriculum using at least the course information being substantially compliant with the course parameters, the course content being selectively customized via the conversational dialog with the generation assistant. The lesson generation component may create lessons for the course content using at least part of the course information. The lessons may be selectively customized via the conversational dialog with the generation assistant. The user interface module may administer the lessons to a learner by interactively presenting questions, collecting responses to the questions, providing feedback to the learner based at least on the responses, and determining a learner level of proficiency of the learner.

[0007] In another aspect, the lessons may be administered to an expert to establish a baseline level of proficiency. The learner level of proficiency may be compared to the baseline level of proficiency to determine a knowledge gap of the learner.

[0008] In another aspect, the lessons may be substantially autonomously customized to the learner to address the knowledge gap via the Al engine as customized lessons. The learner may be administered the customized lessons to improve the learner level of proficiency.

[0009] In another aspect, the Al engine may utilize a large language model (LLM).

[0010] In another aspect, supplied content may be provided to the course repository selectively including documents, videos, audio, slideshows, eBooks, and / or images.

[0011] In another aspect, interacting with the user via the generation assistant comprises utilizing a chat-based interface to engage in targeted follow-up dialog based on the course parameters and user responses.

[0012] In another aspect, course content may include an interactive element generated by the Al engine. In another aspect, the interactive element may include a quiz. Generating the quiz may include identifying an optimal location within the course content for the quiz, analyzing the course information for the lessons preceding the optimal location to determine a quiz subject matter, and substantially automatically formulating quiz questions and corresponding quiz answers based on analysis of the course content relating to the lessons preceding the optimal location. The quiz may include open ended questions, where a learner may provide a freely written answer and the Al engine may analyze the answer in relation to the lessons and / or course. Feedback may be given based on the answer.

[0013] In another aspect, the interactive element may include a trivia game generated by receiving a trivia topic via the user interface module, analyzing the course information within the content repository related to the trivia topic to determine extracted factual information, and generating trivia questions based on the extracted factual information for presentation to the learner in an interactive game format and corresponding trivia answers.

[0014] In another aspect, the interactive element may include a branched decision-making scenario generated by determining a scenario topic within the course parameters, retrieving and analyzing the course information from the content repository relevant to the scenario topic that suggests decision points, decision choices, and decision consequences, and generating the branched decision-making scenario based on at least the decision points, the decision choices, and the decision consequences. The decision-making scenario may be used by performing the steps of recording expert performance data for an expert completing the branched decision-making scenario, recording learner performance data for the learner completing the branched decisionmaking scenario, comparing the learner performance data against the expert performance data to identify decision-making performance gaps, and recommending resources from the content repository to the learner by the Al engine based on the identified performance gaps.

[0015] In another aspect, the branched decision-making scenario may be configured for integration with at least one of a virtual reality (VR) device or augmented reality (AR) device. In another aspect, the Al engine may be used to create an interactive learning activity for the user to include gamification elements, for example, a treasure hunt activity, based on the AR, geolocation, and / or scan activity at a location out in the field.

[0016] In another aspect, the course information may be supplemented by externally sourced information relevant to the course parameters. The Al engine may locate and retrieve the externally sourced information. The course information and the externally sourced information may be used by the Al engine to generate the course content. In another aspect, the externally sourced information may be validated by the Al engine prior to being used to generate the course content. An indication of source of the externally sourced information may be displayed to the user via the user interface to indicate portions of the course content created using the externally sourced information.

[0017] According to an aspect enabled by this disclosure, a method may be provided for conceptualizing and generating an interactive learning course. The method may include storing and organizing course information via a content repository accessible to generate course content, interactively interfacing with a generation assistant via conversational dialog by generating prompts to operate an Al engine, and receiving course objectives indicated by a user and selectively interrogating the user via the generation assistant to refine the course objectives and reach a consensus of course parameters generating the course content of the course curriculum using at least the course information being substantially compliant with the course parameters. The course content may be selectively customized via the conversational dialog with the generation assistant. The method may include creating lessons for the course content using at least part of the course information, the lessons being selectively customized via the conversational dialog with the generation assistant. The method may additionally include administering the lessons to a learner, which may further include interactively presenting questions to the learner, collecting responses to the questions from the learner, providing feedback to the learner based at least on the responses, and determining a learner level of proficiency of the learner.

[0018] In another aspect, the method may include administering the lessons initially to an expert to establish a baseline level of proficiency, determining a knowledge gap of the learner by comparing the learner level of proficiency to the baseline level of proficiency, customizing the lessons to the learner substantially autonomously to address the knowledge gap via the Al engine as customized lessons, and administering the customized lessons to the learner.

[0019] In another aspect, generating the course content may further include generating an interactive element by the Al engine which may include a quiz, trivia, decision-making scenario, or a maze. Generating the quiz may include identifying an optimal location within the course content for the quiz, analyzing the course information for the lessons preceding the optimal location to determine a quiz subject matter for the quiz, and substantially automatically formulating quiz questions and corresponding quiz answers based on analysis of the course content relating to the lessons preceding the optimal location. Generating the trivia game may include receiving a trivia topic for the trivia game, analyzing the course information within the content repository related to the trivia topic to determine extracted factual information, and generating trivia questions based on the extracted factual information for presentation to the learner in an interactive game format and corresponding trivia answers. Generating a branched decision-making scenario may include determining a scenario topic within the course parameters; retrieving and analyzing the course information from the content repository relevant to the scenario topic that suggests decision points, decision choices, and decision consequences; and generating the branched decision-making scenario based on at least the decision points, the decision choices, and the decision consequences. Operating the branched decision-making scenario may include recording expert performance data for an expert completing the branched decision-making scenario, recording learner performance data for the learner completing the branched decisionmaking scenario, comparing the learner performance data against the expert performance data to identify decision-making performance gaps, and recommending by the Al engine resources from the content repository to the learner based on the identified performance gaps.

[0020] In another aspect, the course information may be supplemented by externally sourced information relevant to the course objectives by performing the steps of locating and retrieving by the Al engine the externally sourced information, validating the externally sourced information by the Al engine, generating the course content by the Al engine using the course information and the externally sourced information, and displaying an indication of source of the externally sourced information to the user.

[0021] Terms and expressions used throughout this disclosure are to be interpreted broadly. Terms are intended to be understood respective to the definitions provided by this specification. Technical dictionaries and common meanings understood within the applicable art are intended to supplement these definitions. In instances where no suitable definition can be determined from the specification or technical dictionaries, such terms should be understood according to their plain and common meaning. However, any definitions provided by the specification will govern above all other sources. Various objects, features, aspects, andadvantages described by this disclosure will become more apparent from the following detailed description, along with the accompanying drawings in which like numerals represent like components.BRIEF DESCRIPTION OF THE DRAWINGS

[0022] FIG. 1 is a block diagram view of an illustrative system for conceptualizing and generating an interactive course, according to an embodiment of this disclosure.

[0023] FIG. 2 is a block diagram view of an illustrative course creation system flow, according to an embodiment of this disclosure.

[0024] FIG. 3 is a block diagram view of an illustrative computerized device upon which a system enabled by this disclosure may operate, according to an embodiment of this disclosure.

[0025] FIG. 4 is a flow chart view of an illustrative workflow for generating an interactive course, according to an embodiment of this disclosure.

[0026] FIG. 5 is a flow chart view of an illustrative workflow for determining proficiency and developing customized lessons for a learner, according to an embodiment of this disclosure.

[0027] FIG. 6 is a flow chart view of an illustrative workflow for supplementing course information stored by a content repository with externally sourced information, according to an embodiment of this disclosure.DETAILED DESCRIPTION

[0028] The following disclosure is provided to describe various embodiments of an interactive course building system with generation assistance. Skilled artisans will appreciate additional embodiments and uses of the present invention that extend beyond the examples of this disclosure. Terms included by any claim are to be interpreted as defined within this disclosure. Singular forms should be read to contemplate and disclose plural alternatives. Similarly, plural forms should be read to contemplate and disclose singular alternatives. Conjunctions should be read as inclusive except where stated otherwise. Expressions such as “at least one of A, B, and C” should be read to permit any of A, B, or C singularly or in combination with the remaining elements. Additionally, such groups may include multiple instances of one or more element in thatgroup, which may be included with other elements of the group. All numbers, measurements, and values are given as approximations unless expressly stated otherwise.

[0029] For the purpose of clearly describing the components and features discussed throughout this disclosure, some frequently used terms will now be defined, without limitation. The term prompt, as it is used throughout this disclosure, is defined as an instruction generated to operate the artificial intelligence (Al) engine during conversational dialog. The term artificial intelligence (Al) engine, as it is used throughout this disclosure, is defined as a component operated by prompts from the generation assistant to perform tasks such as generating or customizing course content and lessons, locating external information, and making recommendations. The term user, as it is used throughout this disclosure, is defined as an operator whom interacts with the generation assistant via conversational dialog to indicate course objectives and refine course parameters for course generation. The term learner, as it is used throughout this disclosure, is defined as a person whom is administered lessons for interaction, assessed for proficiency, and resolves knowledge gaps through interaction with customized content. The term expert, as it is used throughout this disclosure, is defined as a person whose performance data, when administered lessons or scenarios, establishes a baseline level of proficiency for comparison with a learner.

[0030] Various aspects of the present disclosure will now be described in detail, without limitation. In the following disclosure, an interactive course building system with generation assistance will be discussed. Those of skill in the art will appreciate alternative labeling of the interactive course building system with generation assistance as an automated course generation platform, course ideation and creation system with generation assistance, generative course creation tool, the invention, or other similar names. Similarly, those of skill in the art will appreciate alternative labeling of the interactive course building system with generation assistance as a method for generating interactive courses using conversational dialog with a generation assistant, course development and personalization method, technique for assisting development and administration of interactive learning courses, method, operation, the invention, or other similar names. Skilled readers should not view the inclusion of any alternative labels as limiting.

[0031] Referring now to FIGS. 1-6, the interactive course building system with generation assistance will now be discussed in more detail. The interactive course building system with generation assistance may include a content repository 110, 210, generation assistant 122, course curriculum development component 124, content generation component 126, lesson generation component 128, user interface module 190, interactive elements 150, 250, and additional components that will be discussed in greater detail below. A backend 120 accessible via a network 180 may be provided to operate the generation assistant 122, course curriculum developmentcomponent 124, content generation component 126, and lesson generation component 128. The interactive course building system with generation assistance may operate one or more of these components interactively with other components for building interactive courses with high efficiency using a generation assistant 122 and Al engine 130, 230 to conceptualize course goals and generate course content and lessons.

[0032] The content repository will now be discussed in greater detail. FIGS. 1-2 highlight examples of the content repository, which may also be discussed along with other figures. A content repository 110, 210 may be able to store and organize course information, which may be accessible to generate course content. Course information may include content supplied by a user or others, for example, selectively including documents, videos, audio, slideshows, eBooks, images, podcasts, and / or additional content that would be apparent to a person of skill in the art after having the benefit of this disclosure. The course repository may be accessible via a network 180, for example, a company network or the Internet.

[0033] The content repository 110, 210 may include existing course information, which may include learning objects and uploaded content by users and live events existing within the system or provided by a user, without limitation. A content repository 110, 210 may include course information, live events, podcast features, and or other content uploaded by a user. An Al engine 130, 230 may process the course information, potentially splitting it into smaller content chunks based on topics or necessity, which may then be stored in the content repository 110, 210. Additional components, for example the content generation component 126, may build new course curricula and / or lessons based on contents in the content repository 110, 210 via Al processing techniques to search for relevant split contents. Furthermore, a content generation component 126 may build new courses based on the processed course information in the content repository 110, 210, potentially using the Al engine 130, 230 processing techniques to search for relevant content.

[0034] The generation assistant and Al engine will now be discussed in greater detail. FIGS. 1-2 highlight examples of the generation assistant, which may also be discussed along with other figures. A generation assistant 122 may interactively interface with a user via conversational dialog, potentially by generating prompts to operate an Al engine 130, 230. In some embodiments, the Al engine 130, 230 may utilize at least one large language model (LLM). Interacting with the user via the generation assistant 122 may comprise utilizing a chat-based interface to engage in targeted follow-up dialog based on the course parameters and user responses.

[0035] The generation assistant 122 may function primarily as a sophisticated intermediary and prompt generator, which may additionally manage the conversational dialog with the user. Forexample, the generation assistant 122 may analyze the user's inputs, interpret their intent regarding course objectives and other course parameters, and translate these high-level requests and ongoing refinements into structured, machine-readable instructions intended for the Al engine 130, 230 as prompts. This translation process may involve parsing natural language, identifying key entities and requirements, and formatting this information according to the unique input requirements of the Al engine 130, 230. Therefore, the generation assistant 122 may act as an intelligent interface layer, facilitating communication between the user and the underlying Al engine 130.

[0036] The generation assistant 122, having formulated a directed prompt based on the conversational dialog and the established course parameters, may then submit this prompt to the Al engine 130, 230. This submission may occur via internal function calls or API requests, depending on the system architecture. The Al engine 130, 230, which may utilize foundational models like large language models (LLMs) or other specialized machine learning models, may receive these precise instructions and undertake the computationally intensive and cognitively complex tasks specified in the prompt. These tasks could include analyzing vast amounts of course information from the content repository 110, 210, synthesizing new textual course content, generating quiz questions, formulating branched scenario logic, or creating multimedia element suggestions based on the provided context and instructions within the prompt.

[0037] This separation of concerns advantageously allows the system to leverage the strengths of different components. For example, the generation assistant 122 may focus on effective user interaction and accurate translation of intent into actionable prompts, while the Al engine 130, 230 may focus on executing those targeted, complex tasks. The prompts generated by the generation assistant 122 may be highly optimized, incorporating context, constraints, and formatting designed to elicit the most accurate and relevant output from the Al engine 130, 230. This structured approach, where carefully crafted prompts instruct a powerful Al engine 130, 230, may enable the system to perform content generation and manipulation tasks with a level of accuracy, speed, consistency, and complexity that may extend beyond the typical capability of a human operator performing the same tasks manually within a similar timeframe. For instance, synthesizing information from up to thousands or millions of documents or generating multiple complex interactive scenarios in hours represents a significant acceleration compared to the months it would typically take a human operator, and with improved outputs. This enhancement in processing information and generating complex, structured output represents an improvement in the functioning of the computer system itself, transforming it into a more powerful and efficient tool for conceptualizing and generating interactive learning courses.

[0038] The Al engine 130, 230 may allow a user to generate course content using externally sourced information 292 to work with or separate from existing course information. Additionally, in cases where the Al engine 130, 230 generates course content from externally sourced information 292, the Al engine 130, 230 may include a source of the information, which may be listed in a reference section. In the same or other embodiments, a user may be able to select a source to be automatically taken to the internet location where the information originated.

[0039] The system components, including the generation assistant 122, course curriculum development component 124, content generation component 126, and lesson generation component 128, operating the Al engine 130, 230, may assist a user in creating a course. Generally, when creating a course 240, a user may be provided with a form via the user interface module 190. For example, the form may prompt a user to enter a course title, the course objectives, the course duration, whether the learner may need to follow a particular path, and / or whether the course is industry specific, without limitation. Those skilled in the art may appreciate the numerous prompts or fields that the form may additionally include, after having the benefit of this disclosure.

[0040] The course curriculum development component 124, using the Al engine 130, 230, may then generate a curriculum for the course based on the information provided. A user may choose to proceed with generating the course content if all requirements, potentially established as course parameters through conversational dialog with the generation assistant 122, appear to be met. The generation assistant 122 may give the user an opportunity to enhance the course curriculum through a chat-based interface before the content generation component 126 generates the course content. The generation assistant 122 may present the user with one or more questions or prompts. Upon response from the user, the generation assistant 122 may use this information to refine the course parameters which guide the course curriculum development component 124 and content generation component 126, while potentially ensuring all listed requirements may be met.

[0041] In various embodiments, once a user is satisfied with the course parameters established via the generation assistant 122, the user may choose to initiate generation of the course content and lessons. The generated output may include details for each lesson in the course, potentially including subtopics. The user may initiate a preview of the generated course content and lessons, which the user may edit to add or remove lessons or subtopics as desired, without limitation. Once a user has optionally confirmed the structure and content, the user may finalize the course generation. This may prompt the content generation component 126 and lesson generation component 128 to generate the final course content for each page of the course.

[0042] In various embodiments, the generation of the course content may take some time to complete. During the generation process, a counter may be shown via the user interface module 190. The counter may show how many pages have been created and / or how many pages are yet to be created, without limitation. Once the course pages have been created, the user may examine each page to view its content. The system may allow users to add different multimedia resources to a course, in addition to the interactive elements 150, 250 the Al engine 130, 230 may generate based on the course information. These elements may include, without limitation, scenario-based assessments (potentially branched decision-making scenarios), storytelling elements, image generation, video generation, and / or quizzes. Those skilled in the art may appreciate the various elements that may be added, after having the benefit of this disclosure.

[0043] In some embodiments, scenario-based assessments may involve the Al engine 130, 230 analyzing the course content and generating realistic scenarios that may bring the course objectives to a different perspective while immersing learners in interactive, real -world scenarios. These scenarios may additionally test the learner's understanding and application of knowledge.

[0044] In additional embodiments, a storytelling element may involve the Al engine 130, 230 assisting the user to create captivating stories and narratives, which may be used as scenarios or case studies within the course. The storytelling feature may also be linked to video generation platforms to enable users to convert the generated stories, scenarios, and / or case studies into captivating videos or animations using various tools.

[0045] In other embodiments, smart image generation may involve the Al engine 130, 230 analyzing page content and recommending relevant images using text-to-image generation or searching an existing corporate library within the content repository 110, 210. This may assist the user by potentially reducing the time and effort needed to manually search for appropriate visuals. A video generator feature may involve the Al engine 130, 230 stitching recommended video clips and / or existing footage from the course information which may create engaging and informative videos. This may leverage diverse video creation tools to tailor the final course content to a user’s specific needs and / or preferences.

[0046] In various embodiments, generating quizzes 152, 252 may involve the Al engine 130, 230 identifying optimal points within the course to introduce quizzes 152, 252 and / or automatically gathering relevant course information for question generation. This may optimally support knowledge retention by strategically placing quizzes 152, 252 that may assess key takeaways from the course content.

[0047] Users may add additional externally sourced information 292 upon demand. Once the user has completed the course review process, the course may then be published to an organization publicly and / or to a marketplace for sales to other learners worldwide.

[0048] The course curriculum development component will now be discussed in greater detail. FIGS. 1-2 highlight examples of the course curriculum development component, which may also be discussed along with other figures. The course curriculum development component 124 may receive course objectives indicated by the user, selectively interrogate the user via the generation assistant 122 to refine the course objectives and reach a consensus of course parameters. For example, a user may enter essential information about the course such as the course title, course objectives, course duration, and learning path to be followed by learners, as well as enter data if the course may be taken by learners from a specific industry.

[0049] With course objectives being defined, user may be presented questions via conversational dialog to refine and determine a consensus of the course parameters. For example, after some iterations with the generation assistant 122, a course curriculum may be created by the course curriculum development component 124 using the Al engine 130, 230 based on input via the generation assistant 122 to be within the course parameters. A course curriculum development component 124 may be designed to create a course curriculum for a course based on the information submitted by the user - through this component, the user may use the Al engine 130, 230, potentially via the generation assistant 122 and conversational dialog, to enhance and enrich the course curriculum.

[0050] In various embodiments, the course creator may require a strong foundation of accurate and up-to-date information. This information may be collected from reputable articles, academic research, and journals, as well as from subject matter experts through interviews, however, those skilled in the art will appreciate the various other sources that may be used to collect information from, after having the benefit of this disclosure.

[0051] Additionally, the user may collect the supplied content from a variety of resources. These resources may include, without limitation, PDFs, electronic books, word documents, video files, audio files, slide shows, other courses potentially generated by an Al engine 130, 230, and SCORM courses. Those skilled in the art may appreciate the various resources that may be used to collect course information from, after having the benefit of this disclosure. In the same or other embodiments, these resources may be taken from internal course information and / or externally sourced information 292. In various embodiments, video files may include video recordings and / ortranscripts. In the same or other embodiments, audio files may include recorded audio and / or audio transcripts from multiple sources, such as podcasts, without limitation.

[0052] The system may allow a user to rapidly generate course content by inputting course information from supplied content such as video lectures, PowerPoint slides and Word documents. Users may also tap on innovative instructional frameworks to train competencies and expertise and adopt localized features to contextualize the course content for training.

[0053] The system may allow the user to use a Podcast feature as the tool of content provider which may include the knowledge of experts via their interviews. For example, an organization senior employee may have vast knowledge and wants to teach subordinates the same but is not able to find the time; they may give an interview as podcast and the knowledge may become course information for the system which may generate the course content accordingly.

[0054] The system may be configured with unique functionalities to provide a framework that may generate a well -structured course curriculum on the basis of input of various information provided by the user while completing the form and / or during conversational dialog with the generation assistant 122 built within the system to understand the user’s preferences. The structured course curriculum may be generated using the content repository 110, 210 already integrated with the Al engine 130, 230, supplied content, existing course information in the system, previous courses made, podcast features, etc. within the system. The technology may aid in using the vast amount of course information shared and used within the system as well.

[0055] The structured course curriculum generated by the Al engine 130, 230 may be selectively customized by the user, for example, edited, reviewed, and retrieved by the user. On the basis of the finalized tailored course curriculum after the edit or review of the user, then the course content may be generated using the Al engine 130, 230 which may be selectively customized by the user within each topic of the course curriculum. The selectively customized course content may be planned into lessons as per the preference of the user. The system allows the user to choose layouts to create the lesson for specific topic within the content generated for the course. Each lesson may include interactive elements 150, 250. Lessons may include one or more pages. Each page may also include interactive elements 150, 250 selected as per the preference of the course for each lesson. Pages can be individually assessed, edited, and tailored by the user, for example, via a form that will help to set up the content manipulations and consult the Al engine 130, 230 to generate the lessons and corresponding course content.

[0056] The content generation component will now be discussed in greater detail. FIGS. 1-2 highlight examples of the content generation component, which may also be discussed alongwith other figures. The content generation component 126 may generate the course content of the course curriculum using at least the course information being substantially compliant with the course parameters, the course content being selectively customized via the conversational dialog with the generation assistant 122. In some embodiments, course information may be supplemented by externally sourced information 292 relevant to the course parameters, which the Al engine 130, 230 may locate and retrieve from external sources, for example, sources available via the Internet. Externally sourced information 292 may be validated by the Al engine 130, 230 prior to being used to generate the course content to ensure course accuracy. In some embodiments, an indication of source of the externally sourced information 292 may be displayed to the user via the user interface to indicate portions of the course content created using the externally sourced information 292, links to those external sources if available, and other information that can help a user to validate the externally sourced information 292.

[0057] A content generation component 126 may be configured to retrieve course information from a content repository 110, 210, potentially including supplied content like documents, videos, audio, PowerPoint presentations, eBooks, or other interactive learning courses, and potentially externally sourced information 292. The course information may be broken down, analyzed, and added to a course, potentially organized into lessons and pages within lessons. A story-telling feature may be designed to develop captivating stories, including branched decisionmaking scenarios 156, 256. Case studies based on information provided by the user, including the stories, case studies, or scenarios, may be added to the course content as text or converted to videos.

[0058] In various embodiments, the generation of the course content may take some time to complete. During the generation process, a counter may be shown via the user interface module 190. The counter may show how many pages have been created and / or how many pages are yet to be created, without limitation. Once the course pages have been created, the user may examine each page to view its content. The system may allow users to add different multimedia resources to a course, in addition to the interactive elements 150, 250 the Al engine 130, 230 may generate based on the course information. These elements may include, without limitation, scenario-based assessments (potentially branched decision-making scenarios 156, 256), storytelling elements, image generation, video generation, and / or quizzes 152, 252.

[0059] In a few embodiments, scenario-based assessments may involve the Al engine 130, 230 analyzing the course content and generating realistic scenarios that may bring the course objectives to a different perspective while immersing learners in interactive, real -world scenarios. These scenarios may additionally test the learner's understanding and application of knowledge. In additional embodiments, this storytelling feature, potentially part of the content generationcomponent 126, may also be linked to video generation platforms to enable users to convert the generated stories, scenarios, and / or case studies into captivating videos or animations using various tools. In other embodiments, smart image generation may involve the Al engine 130, 230 analyzing page content and recommending relevant images using text-to-image generation or searching an existing corporate library within the content repository 110, 210. This may assist the user by potentially reducing the time and effort needed to manually search for appropriate visuals.

[0060] In numerous embodiments, a video generator feature may involve the Al engine 130, 230 stitching recommended video clips and / or existing footage from the course information which may create engaging and informative videos. This may leverage diverse video creation tools to tailor the final course content to a user’ s needs and / or preferences. Additionally, media elements may be created from this course content. Generated media content may be shortened to make an interactive learning course duration a desired length and within a desired timeframe.

[0061] In one example embodiment, a system for conceptualizing and generating an interactive learning course using an Al engine 130, 230 may be used with a variety of supplied content uploaded in a content repository 110, 210 in the form of PDF and Word documents, eBooks, video files (uploaded and recorded from Live Events), audio files (uploaded and recorded from Podcasts), PowerPoint presentations, other interactive learning courses, as well as externally sourced information 292. The system may include a user interface module 190 where the user enters crucial information about the course such as the course title, course objectives, course duration, and learning path potentially followed by a learner, as well as entry of data if the course may be taken by a learner from a specific industry, a course curriculum development component 124 designed to create a course curriculum for a course based on the information submitted by the user in the previous step. Through this component, the user may use the Al engine 130, 230 to enhance and enrich the course curriculum through conversational dialog via the generation assistant 122, a content generation component 126 configured to retrieve course information from a content repository 110, 210 consisting of supplied content like documents, videos, audio, PowerPoint presentations, eBooks, externally sourced information 292 (at the click of a button), and other interactive learning courses 240. The course information may be broken down, analyzed, and added to a course, organized into lessons and pages within lessons.

[0062] The lesson generation component will now be discussed in greater detail. FIGS. 1 highlight examples of the lesson generation component, which may also be discussed along with other figures. The lesson generation component 128 may create lessons for the course content using at least part of the course information to ensure the lessons are within the course parameters. The lessons may be created and / or selectively customized using conversational dialog with thegeneration assistant 122. The system may employ an Al engine 130, 230 via the content generation component 126 employing advanced search algorithms to autonomously source and synthesize relevant educational resources from diverse repositories to generate the course content for each topic of the course curriculum. The system may utilize a versatile lesson generation component 128, which may offer multiple layout options and multimedia integration tools for crafting engaging and interactive lessons by integrating different numbers of pages in the lesson.

[0063] The lesson may be of various types including quizzes 152, 252, interactive elements150, 250, bullet points, text, or other content. The system may include an Al engine 130, 230 driven multimedia integration system that may incorporate textual, graphical, and interactive elements 150, 250 into pages of the lesson. Those multimedia elements may be manually added, images may be edited using external tools, or the media may be generated by giving descriptions to the Al engine 130, 230 or via conversational dialog with the generation assistant 122 to generate the type of image, video, or audio as required by the user. Quizzes may include open ended questions, where a learner may provide a freely written answer and the Al engine may analyze the answer in relation to the lessons and / or course. Feedback may be given based on the answer.

[0064] The system along with media integration may offer a way of engaging learners with application of dynamic quizzes, simulations, and augmented reality scenarios through an interactive assessment and augmented reality system as interactive elements 150, 250. In one example, the Al engine may be used to create an interactive learning activity for the user to include gamification elements, for example, a treasure hunt activity, based on the AR, geolocation, and / or scan activity at a location out in the field. The system also may provide a comprehensive course publishing and management system for effective deployment, distribution, and optimization of course content. The system may allow the user to add / customize the grid, text, images, video, audio, augmented reality elements, quiz, annotation, background in lessons and their pages. The user may manually upload the multimedia or create images, video, audio, and quiz using the Al engine 130, 230. The quiz generated by the user may be used for assessment, which may be manually added by the user, or the question may be generated using the Al engine 130, 230. The interactive learning course created may be uploaded to the organization or marketplace. The interactive learning course created by such method may be cloned, edited, and updated as the interactive learning course created may serve as the course information for creating other updated courses on a same or similar topic within the system.

[0065] In one embodiment, the lessons may be administered to an expert to establish a baseline level of proficiency. The learner may also be administered the lessons to assess a learner level of proficiency, which may be compared to the baseline level of proficiency to determine aknowledge gap of the learner. The lessons may then be substantially autonomously customized to the learner to address the knowledge gap via the Al engine 130, 230 as customized lessons. The learner may then be administered the customized lessons to close the knowledge gap.

[0066] The subscription component will now be discussed in greater detail, without limitation. A subscription module may be included that allows organizations to subscribe to externally sourced information 292 from other organizations within the platform. This may enable organizations to benefit from course information or course content that other organizations have already developed, potentially at a lower cost than creating this course content from scratch.

[0067] The user interface module will now be discussed in greater detail. FIG. 1 highlights examples of the user interface module. In one embodiment, the user interface module 190 may administer the lessons to a learner comprising interactively presenting questions, collecting responses to the questions, providing feedback to the learner based at least on the responses, and determining a learner level of proficiency. Additionally, the user interface module 190 may serve as the primary point of interaction for the system's various components. For a user engaged in conceptualizing and generating an interactive learning course, the user interface module 190 may present forms for receiving initial course parameters, such as the course title, course objectives, duration, intended learner path, and industry specificity. It may also facilitate the conversational dialog with the generation assistant 122 through an integrated chat-based interface, allowing the user to refine course objectives and reach a consensus on course parameters. Furthermore, the user interface module 190 may be the means through which the user previews, reviews, and edits the generated course curriculum, course content, and lessons. It may provide tools for adding, removing, or modifying text, multimedia elements (images, video, audio), and interactive elements 150, 250 like quizzes 152, 252 or branched decision-making scenarios 156, 256. During potentially time-consuming processes like course content generation, the user interface module 190 may display progress indicators, such as counters showing page generation status. It may also be used to initiate the finalization and publishing of the completed interactive learning course.

[0068] For a learner, the user interface module 190 may administer the lessons of the interactive learning course. This administration may involve interactively presenting questions or interactive elements 150, 250, collecting responses from the learner, and potentially providing feedback based on those responses. The user interface module 190 may also display course content, including text, multimedia, and potentially indications of source for any externally sourced information 292 used within the course content.

[0069] The interactive elements will now be discussed in greater detail. FIGS. 1-2 highlight examples of the interactive elements, which may also be discussed along with other figures. A system enabled by this disclosure may incorporate various interactive elements 150, 250 designed to engage the learner and assess their understanding of the course content. These elements may move beyond passive consumption of information, requiring the learner to actively participate, make decisions, or recall knowledge. The Al engine 130, 230 may play a significant role in generating these interactive elements 150, 250, potentially analyzing the surrounding course content to ensure relevance and effectiveness. The user interface module 190 may then present these interactive elements 150, 250 to the learner and collect their responses for evaluation or feedback. Examples of such elements may include quizzes 152, 252, trivia games 154, 254, and branched decision-making scenarios 156, 256.

[0070] A quiz may serve as a common form of interactive element used for knowledge assessment within a lesson or at the end of a module. Generating a quiz may involve the Al engine 130, 230 identifying an optimal location within the course content based on teaching principles or user instruction. The Al engine 130, 230 may then analyze the course information related to the lessons preceding the identified location to determine the relevant subject matter. Based on this analysis, the Al engine 130, 230 may substantially automatically create quiz questions and corresponding quiz answers. The user may also have the option to manually create or edit quiz questions via the user interface module 190.

[0071] When a learner encounters a quiz within an interactive learning course, the user interface module 190 may present the quiz questions one or multiple at a time. For each question, the learner may be presented with the question text and potential answer options (e.g., multiplechoice selections, true / false buttons, a text input field). The learner may interact with the user interface module 190 to select or input their answer. Upon submission of the answer or completion of the quiz, the system, potentially via the user interface module 190, may provide immediate or delayed feedback, which could include indicating correctness, displaying the correct answer, providing explanatory text, and updating a score or progress indicator.

[0072] A trivia game may represent another type of interactive element, potentially offering a more gamified approach to knowledge reinforcement. Generating a trivia game may begin with receiving a trivia topic, possibly from the user via the user interface module 190. The Al engine 130, 230 may then analyze course information within the content repository 110, 210 related to the specified trivia topic. This analysis may focus on determining extracted factual information. Based on this extracted information, the Al engine 130, 230 may generate trivia questions and corresponding trivia answers, which can be presented to the learner in an interactivegame format, potentially including scoring or timed elements. In one example, a trivia game may be a resource to test the knowledge of the learner in the organization. A trivia game may be created, scored, and / or used to test a learner's skills on various subjects. Additionally, a trivia game may facilitate the creation of relevant resources to address a knowledge gap, which may optimally equip a learner with the knowledge and skills they may need to be successful.

[0073] In various embodiments, using the Al engine 130, 230, organizations may create a trivia game based on the course information in the organization including previously supplied content like PDFs, e-books, word documents, uploaded video files, recorded live events, uploaded audio files, recorded podcasts, presentations, other interactive learning courses 240, and / or credible externally sourced information 292. The trivia game may generate trivia questions in a game format from the course information that the Al engine 130, 230 may generate. The trivia questions may gauge the learner's understanding of the topics covered. The trivia game may additionally assist the management team to identify the knowledge gap in the organization and / or may recommend specific resources to help close the identified knowledge gap.

[0074] In one example, the system may be utilized by corporations to transform course information into an engaging trivia game. This game may generate trivia questions in a fun, interactive format, potentially tailored to the knowledge requirements of the learner. The trivia questions may be created by the Al engine 130, 230 that may adapt to the learner's learning needs. On one hand, this may allow HR to gauge the skill level and knowledge of the learner. On the other hand, it may provide the learner with personalized recommendations for specific courses, podcasts, or other resources to bridge their knowledge gap. In at least one example, the Al engine 130, 230 may recommend resources to the learner that may assist with closing a knowledge gap, including tailored courses for or related to the gap.

[0075] Branched decision-making scenarios 156, 256 may offer an immersive interactive element. Generation may start by determining a scenario topic, potentially derived from the course parameters. The Al engine 130, 230 may then retrieve and analyze course information from the content repository 110, 210 relevant to the scenario topic. This analysis may suggest potential decision points a learner might face, plausible decision choices at each point, and the logical decision consequences, outcomes, feedback, and / or progression to different scenario states for each choice. Based on at least these suggested decision points, choices, and consequences, the Al engine 130, 230 may generate the structure and narrative for the branched decision-making scenario. This type of interactive element allows learners to explore the results of different decisions in a simulated environment, potentially enhancing understanding and practicalapplication of the course content. These scenarios may also be used for comparative assessment by recording performance data from experts and learners to identify potential performance gaps.

[0076] In one example, branched decision-making scenarios 156, 256 may allow a learner to make choices and experience the consequences of the choices, which may foster deeper engagement and / or knowledge retention. The platforms enabling branched decision-making scenarios 156, 256 may be used with the Al engine 130, 230 to potentially reduce the time required to create the branched decision-making scenarios 156, 256. In at least one embodiment, the branched decision-making scenario may be used with a virtual reality, augmented reality, and / or other simulated reality device.

[0077] When a learner or expert engages with a branched decision-making scenario, the user interface module 190 may present the scenario context, often involving narrative text, images, or video. At decision points, the user interface module 190 may display the available decision choices. The individual then selects a choice, typically via clicking a button or another interactive control. Based on the selected choice, the system may progress the scenario, which may involve presenting new information, showing the immediate consequences of the choice (positive or negative feedback), or moving to a subsequent decision point. The learner or expert continues making choices, navigating a path through the scenario until reaching a conclusion or outcome state. The experience may differ slightly depending on the role — an expert might navigate it to establish a baseline level of proficiency, while a learner navigates it for practice and assessment, potentially receiving feedback and recommendations based on comparison to the baseline or defined optimal paths.

[0078] Mazes, potentially related to or a specific implementation of branched decisionmaking scenarios 156, 256, may also serve as an interactive element. Creating diverse scenarios for these mazes 158, 258 may traditionally be time-consuming. However, the system, potentially utilizing the Al engine 130, 230 and the content generation component 126, may significantly reduce this timeframe. Once maze scenarios are established, experts within an organization may navigate through them, with outcomes recorded to serve as a benchmark. Subsequent learners engaging with the maze may then have their performance compared to the benchmark, allowing for the identification of performance disparities and potentially receiving tailored recommendations from the Al engine 130, 230 to address identified gaps. In some embodiments, maze features may be integrated with virtual reality or augmented reality devices.

[0079] Referring now to FIG. 3, an illustrative computerized device will be discussed, without limitation. Various aspects and functions described in accord with the present disclosuremay be implemented as hardware or software on one or more illustrative computerized devices. There are many examples of illustrative computerized devices 300 currently in use that may be suitable for implementing various aspects of the present disclosure. Some examples include, among others, network appliances, personal computers, workstations, mainframes, networked clients, servers, media servers, application servers, database servers and web servers. Other examples of illustrative computerized devices 300 may include mobile computing devices, cellular phones, smartphones, tablets, video game devices, personal digital assistants, network equipment, devices involved in commerce and associated illustrative computerized device 300, among others. Additionally, aspects in accord with the present disclosure may be located on a single illustrative computerized device 300 or may be distributed among one or more illustrative computerized devices 300 connected to one or more communication networks.

[0080] For example, various aspects and functions may be distributed among one or more illustrative computerized devices 300 configured to provide a service to one or more client computers, or to perform an overall task as part of a distributed system. Additionally, aspects may be performed on a client-server or multi-tier system that include components distributed among one or more server systems that perform various functions. Thus, the disclosure is not limited to executing on any particular system or group of systems. Further, aspects may be implemented in software, hardware or firmware, or any combination thereof. Thus, aspects in accord with the present disclosure may be implemented within methods, acts, systems, system elements and components using a variety of hardware and software configurations, and the disclosure is not limited to any particular distributed architecture, network, or communication protocol.

[0081] FIG. 3 shows a block diagram of an illustrative computerized device 300, in which various aspects and functions of the present disclosure may be practiced. The illustrative computerized device may include one or more illustrative computerized devices 300. Examples of device 300 include servers, clients, PCs, mobile devices, and commerce equipment, without limitation. Implementation can occur on a single device 300 or be distributed across multiple devices connected via networks. Functions can be distributed among devices, such as in clientserver or multi-tier systems. Implementation may use software, hardware, firmware, or combinations, without being limited to particular architectures, networks, or protocols.

[0082] FIG. 3 diagrams the illustrative computerized device 300, potentially comprising multiple devices interconnected via a communication network 308 for wired or wireless data exchange. Network 308 enables data exchange between devices using various protocols (e.g., Ethernet, Wi-Fi, TCP / IP, HTTP) and security measures (e.g., TSL, SSL, VPN). Network 308 can connect numerous devices via diverse media and protocols. Aspects can be implemented on device300, which includes processor 310, memory 312, bus 314, I / O interface 316, storage system 318, network communication device 320, and optional additional devices 322. Processor 310 executes instructions and connects to elements like memory 312 via bus 314. The network communication device 320 manages data transfer between device 300 components and external servers 332, databases 334, smartphones 336, and other computerized devices 338 over network 308. Device 300 may serve to analyze / communicate data among connected devices 332, 334, 336, 338. Device 300 may communicate with connected devices 332, 334, 336, 338 via network 308 (e.g., internet, WLAN) using its network communication device 320, through wired or wireless connections.

[0083] Memory 312 stores programs and data for device 300 operation, potentially using volatile (DRAM, SRAM) or non-volatile storage, and may have specialized structures. Components of device 300 are linked by bus 314, which represents various physical or standard communication links (e.g., USB, SATA, PCI) for data and instruction exchange. Device 300 features interface devices 316 (input / output like keyboards, screens, speakers) enabling interaction with external entities. Storage system 318 uses nonvolatile media (e.g., disk, flash) for persistent storage of instructions and data. Processor 310 may transfer data between storage 318 and memory 312 for processing.

[0084] The device 300 in FIG. 3 is exemplary; aspects can be implemented on other configurations, including specialized hardware or general -purpose devices. Device 300 may use an operating system, executed by processor 310, to manage hardware, where various OS types are possible. The processor and OS form a platform for applications in diverse programming languages (e.g., JAVA, C++, Python), communicating over networks via protocols like TCP / IP. Other programming paradigms are also applicable. Implementations might use non-programmed environments (e.g., HTML, XML) or combine programmed and non-programmed elements. An illustrative computerized device may use existing commercial software (e.g., Database Management Systems) and perform functions beyond this disclosure's scope.

[0085] In operation, a method may be provided for building interactive courses with high efficiency using a generation assistant to conceptualize course goals and generate course content and lessons. Those of skill in the art will appreciate that the following methods are provided to illustrate an embodiment of the disclosure and should not be viewed as limiting the disclosure to only those methods or aspects. Skilled artisans will appreciate additional methods within the scope and spirit of the disclosure for performing the operations provided by the examples below after having the benefit of this disclosure. Such methods are intended to be included by this disclosure.

[0086] In one example, creating an interactive learning course using traditional flow may involve sourcing course information from experts. This process may include interviewing these experts and requesting their time investment, which may extend over several weeks. This may traditionally present a bottleneck in course production. However, the system described herein may aim to reduce this time to a few hours. It may achieve this by utilizing an Al engine and an efficient course information collection method, enabling the Al engine via the content generation component and lesson generation component to generate the course content effectively.

[0087] The content generation component or lesson generation component using the Al engine may retrieve course information from the content repository and / or other data centers that may be appreciated by those of skill in the art. The content repository may house course information from various sources, including edited and non-edited files such as office documents and PDFs as supplied content. In addition, it may contain video and audio files, as well as other e- leaming formats like SCORM, which may be processed into the content repository.

[0088] To help ensure the course information is up-to-date, the system may provide a podcast feature. This feature may serve as a platform for users. The podcasts, whether used immediately or after editing, may be combined with the course information from the content repository. This enriched course information may then be utilized during the conceptualizing and generating process. Course information, such as from live events, podcasts, and other events may be used. Records may be collected, and new course content may be created from it.

[0089] The Al engine may use additional course information or externally sourced information. While the user interacts with the system, it may generate course content from externally sourced information and combine it with the existing course information the organization has. This course content may require approval from the content owners before use. It may combine externally sourced information but provide an indication of source or citation to the original resources so a request to use may be performed.

[0090] Additional externally sourced information may be acquired by offering access to extra resources when generating the interactive learning course, like externally sourced information from universities, training companies, and such. The system may then produce a report showing how much this externally sourced information is used and potentially reward the knowledge owner. Simultaneously, using organization course information, the company's HR may decide to reward some individuals who contribute supplied content to the system by performing approved podcasts or adding eBooks, and articles.

[0091] The outcome of the initial process may involve creating an updated course curriculum with its topics and subtopics. This may be based on organizational course information or other sources, such as externally sourced information or third-party approved content. The content generation component or lesson generation component may take this course curriculum and generate course content divided into lessons and pages. Each individual page may be selectively customized by the user via the user interface module interacting with the Al engine. Unlike some methods that allow the addition of different multimedia resources to a course, the system may create interactive elements according to the user’s provided course parameters and existing course information. The Al engine may analyze the course content to recommend and create scenarios for a scenario-based assessment, such as via branched decision-making scenarios.

[0092] For storytelling, the Al engine may analyze the course content and recommend and create a story. This story may be manually converted to a video / animation or with the support of other text-to-video modules or integrated tools. The same process may apply to case studies. The Al engine may analyze the course content and recommend and create a story. This story may be converted to a video / animation, for example, via other text-to-video modules or integrated tools.

[0093] For choosing images, the Al engine may analyze page content to generate a prompt, which may then be used to recommend or generate the relevant image to add to that page. This may be done using a text-to-image technique or by searching images within the content repository. The video generator feature may use one of the modules above or combine recommended existing video clips together, or both, to create the relevant video.

[0094] For quizzes, when the user indicates a desire to add a quiz, the Al engine may check where it might need to be added. Accordingly, it may gather the relevant course information to generate the relevant quiz. Quizzes may include open ended questions, where a learner may provide a freely written answer and the Al engine may analyze the answer in relation to the lessons and / or course. Feedback may be given based on the answer.

[0095] An additional embodiment of a system for conceptualizing and generating an interactive learning course enabled by this disclosure will now be discussed, without limitation. A framework for an interactive learning course system and method may be described herein. In particular, the present disclosure may relate to a system and method configured to rapidly conceptualize and generate interactive learning courses in an intelligent and learning effective manner through the system platform.

[0096] Referring now to flowchart 400 of FIG. 4, an example method for an illustrative workflow for generating an interactive course will be described, without limitation. Starting withBlock 402, the operation may begin with a user engaging the generation assistant via conversational dialog to establish course objectives and refine them into agreed-upon course parameters. (Block 404). The generation assistant may then generate prompts to operate the artificial intelligence (Al) engine, which may retrieve relevant course information and externally sourced information from the content repository or other sources. (Block 406). Using the course parameters and retrieved information, the Al engine, potentially directed by the course curriculum development component, may generate a draft course curriculum. (Block 408). The user, via the user interface module, may review and selectively customize this draft course curriculum. (Block 410). Following curriculum finalization, the Al engine, potentially via the content generation component, may generate course content and recommend or create associated interactive elements like quizzes, trivia, or branched decision-making scenarios. (Block 412). Finally, the user may review and selectively customize the generated course content and interactive elements before the lesson generation component assembles the final lessons for the interactive learning course.

[0097] Referring now to flowchart 500 of FIG. 5, an example method for an illustrative workflow for determining proficiency and developing customized lessons for a learner will be described, without limitation. Starting with Block 502, the operation may begin by administering lessons or specific interactive elements, such as quizzes or branched decision-making scenarios, to an expert via the user interface module to record performance data and establish a baseline level of proficiency. (Block 504). Subsequently, the same lessons or interactive elements may be administered to a learner via the user interface module. The user interface module may then collect responses to questions or record performance data from the learner's interaction with the lessons or interactive elements. Based on the collected responses or performance data, the system may determine the learner level of proficiency. (Block 506). This determined learner level of proficiency or performance data may then be compared against the previously established baseline level of proficiency from the expert. (Block 508). Based on the comparison, the system may determine the knowledge gap of the learner. (Block 510). A customized lesson may be generated for the learner with consideration of their learner level of proficiency and associated knowledge gap. (Block 512). The learner may then be administered the customized lesson to resolve the knowledge gap. (Block 514). The operation may then end at Block 520.

[0098] Referring now to flowchart 600 of FIG. 6, an example method for an illustrative workflow for supplementing course information stored by a content repository with externally sourced information will be described, without limitation. Starting with Block 602, the operation may begin as the Al engine, during course content generation or based on a user request via the generation assistant, determines that internal course information from the content repository maybe insufficient to meet the course parameters. (Block 604). The Al engine may then locate potentially relevant externally sourced information from external sources (like the internet or subscribed libraries) based on the course parameters or specific prompts. (Block 606). The Al engine may then retrieve the externally sourced information. (Block 608). The retrieved externally sourced information may then be validated by the Al engine, potentially applying defined rules or filters. (Block 610). The Al engine may then generate course content using both the internal course information and the validated externally sourced information. (Block 612). Finally, the system, potentially via the user interface module, may display an indication of source for portions of the course content created using the externally sourced information. (Block 614). The operation may then end at Block 620.

[0099] While various aspects have been described in the above disclosure, the description of this disclosure is intended to illustrate and not limit the scope of the invention. The invention is defined by the scope of the appended claims and not the illustrations and examples provided in the above disclosure. Skilled artisans will appreciate additional aspects of the invention, which may be realized in alternative embodiments, after having the benefit of the above disclosure. Other aspects, advantages, embodiments, and modifications are within the scope of the following claims.

Claims

CLAIMSWhat is claimed is:

1. A system for conceptualizing and generating an interactive learning course comprising: a content repository to store and organize course information accessible to generate course content; a generation assistant to interactively interface with a user via conversational dialog by generating prompts to operate an artificial intelligence (Al) engine; a course curriculum development component to receive course objectives indicated by the user, selectively interrogate the user via the generation assistant to refine the course objectives and reach a consensus of course parameters; a content generation component to generate the course content of the course curriculum using at least the course information being substantially compliant with the course parameters, the course content being selectively customized via the conversational dialog with the generation assistant; a lesson generation component to create lessons for the course content using at least part of the course information, the lessons being selectively customized via the conversational dialog with the generation assistant; a user interface module to administer the lessons to a learner comprising interactively presenting questions to the learner, collecting responses to the questions from the learner, providing feedback to the learner based at least on the responses, and determining a learner level of proficiency of the learner.

2. The system of claim 1 : wherein the lessons are administered to an expert to establish a baseline level of proficiency; and wherein the learner level of proficiency is compared to the baseline level of proficiency to determine a knowledge gap of the learner.

3. The system of claim 2: wherein the lessons are substantially autonomously customized to the learner to address the knowledge gap via the Al engine as customized lessons; and wherein the learner is administered the customized lessons to improve the learner level of proficiency.

4. The system of claim 1, wherein the Al engine utilizes at least one large language model (LLM).

5. The system of claim 1, wherein supplied content is provided to the course repository selectively comprising documents, videos, audio, slideshows, eBooks, and / or images.

6. The system of claim 1, wherein interacting with the user via the generation assistant comprises utilizing a chat-based interface to engage in targeted follow-up dialog based on the course parameters and user responses.

7. The system of claim 1, wherein the course content comprises an interactive element generated by the Al engine.

8. The system of claim 7, wherein the interactive element comprises a quiz, and wherein generating the quiz comprises: identifying an optimal location within the course content for the quiz; analyzing the course information for the lessons preceding the optimal location to determine a quiz subject matter for the quiz; and substantially automatically formulating quiz questions and corresponding quiz answers based on analysis of the course content relating to the lessons preceding the optimal location.

9. The system of claim 7, wherein the interactive element comprises a trivia game, and wherein generating the trivia game comprises: receiving a trivia topic for the trivia game via the user interface module; analyzing the course information within the content repository related to the trivia topic to determine extracted factual information; and generating trivia questions based on the extracted factual information for presentation to the learner in an interactive game format and corresponding trivia answers.

10. The system of claim 7, wherein the interactive element comprises a branched decision-making scenario generated by performing the steps: determining a scenario topic within the course parameters; retrieving and analyzing the course information from the content repository relevant to the scenario topic that suggests decision points, decision choices, and decision consequences; generating the branched decision-making scenario based on at least the decision points, the decision choices, and the decision consequences; and wherein administering the decision-making scenario comprises: recording expert performance data for an expert completing the branched decisionmaking scenario, recording learner performance data for the learner completing the branched decision-making scenario, comparing the learner performance data against the expert performance data to identify decision-making performance gaps, and recommending resources from the content repository to the learner by the Al engine based on the identified performance gaps.

11. The system of claim 10, wherein the branched decision-making scenario is configured for integration with at least one of a virtual reality (VR) device or augmented reality (AR) device.

12. The system of claim 1 : wherein the course information is supplemented by externally sourced information relevant to the course parameters; wherein the Al engine locates and retrieves the externally sourced information; and wherein the course information and the externally sourced information are used by the Al engine to generate the course content.

13. The system of claim 12: wherein the externally sourced information is validated by the Al engine prior to being used to generate the course content; and wherein an indication of source of the externally sourced information is displayed to the user via the user interface to indicate portions of the course content created using the externally sourced information.

14. An interactive learning course assisted generation system comprising: a content repository to store and organize course information accessible to generate course content; a generation assistant to interactively interface with a user via conversational dialog by generating prompts to operate an artificial intelligence (Al) engine utilizing a chat-based interface to engage in targeted follow-up dialog based on course parameters and user responses; a course curriculum development component to receive course objectives indicated by the user, selectively interrogate the user via the generation assistant to refine the course objectives and reach a consensus of the course parameters; a content generation component to generate the course content of the course curriculum using at least the course information being substantially compliant with the course parameters, the course content being selectively customized via the conversational dialog with the generation assistant;a lesson generation component to create lessons for the course content using at least part of the course information, the lessons being selectively customized via the conversational dialog with the generation assistant; wherein the lessons are administered to an expert to establish a baseline level of proficiency and administered to a learner to determine an learner level of proficiency that is compared to the baseline level of proficiency to determine a knowledge gap of the learner, the lessons being substantially autonomously customized to the learner to address the knowledge gap via the Al engine as customized lessons to be subsequently administered to the learner to improve the learner level of proficiency; and wherein the course information is supplemented by externally sourced information located and retrieved by the Al engine relevant to the course parameters, the course information and the externally sourced information being used by the Al engine to generate the course content.

15. The system of claim 14, wherein the course content comprises an interactive element generated by the Al engine comprising one or more of: a quiz, wherein generating the quiz comprises: identifying an optimal location within the course content for the quiz, analyzing the course information for the lessons preceding the optimal location to determine a quiz subject matter for the quiz, and substantially automatically formulating quiz questions and corresponding quiz answers based on analysis of the course content relating to the lessons preceding the optimal location; a trivia game, wherein generating the trivia game comprises: receiving a trivia topic for the trivia game, analyzing the course information within the content repository related to the trivia topic to determine extracted factual information, and generating trivia questions based on the extracted factual information for presentation to the learner in an interactive game format and corresponding trivia answers; and / or a branched decision-making scenario generated by performing the steps:determining a scenario topic within the course parameters, retrieving and analyzing the course information from the content repository relevant to the scenario topic that suggests decision points, decision choices, and decision consequences, generating the branched decision-making scenario based on at least the decision points, the decision choices, and the decision consequences, and wherein administering the decision-making scenario comprises: recording expert performance data for the expert completing the branched decision-making scenario, recording learner performance data for the learner completing the branched decision-making scenario, comparing the learner performance data against the expert performance data to identify decision-making performance gaps, and recommending by the Al engine resources from the content repository to the learner based on the identified performance gaps.

16. The system of claim 14, further comprising a user interface module to administer the lessons to the learner.

17. A method for conceptualizing and generating an interactive learning course comprising: storing and organizing course information a content repository accessible to generate course content; interactively interfacing with a generation assistant via conversational dialog by generating prompts to operate an artificial intelligence (Al) engine; receiving course objectives indicated by a user and selectively interrogating the user via the generation assistant to refine the course objectives and reach a consensus of course parameters; generating the course content of the course curriculum using at least the course information being substantially compliant with the course parameters, the course content being selectively customized via the conversational dialog with the generation assistant;creating lessons for the course content using at least part of the course information, the lessons being selectively customized via the conversational dialog with the generation assistant administering the lessons to a learner comprising: interactively presenting questions to the learner, collecting responses to the questions from the learner, providing feedback to the learner based at least on the responses, and determining a learner level of proficiency of the learner.

18. The method of claim 17, further comprising: administering the lessons initially to an expert to establish a baseline level of proficiency; determining a knowledge gap of the learner by comparing the learner level of proficiency to the baseline level of proficiency; customizing the lessons to the learner substantially autonomously to address the knowledge gap via the Al engine as customized lessons; and administering the customized lessons to the learner.

19. The system of claim 17, wherein generating the course content further comprises generating an interactive element by the Al engine to comprise one or more of: a quiz, wherein generating the quiz comprises: identifying an optimal location within the course content for the quiz, analyzing the course information for the lessons preceding the optimal location to determine a quiz subject matter for the quiz, and substantially automatically formulating quiz questions and corresponding quiz answers based on analysis of the course content relating to the lessons preceding the optimal location; a trivia game, wherein generating the trivia game comprises: receiving a trivia topic for the trivia game,analyzing the course information within the content repository related to the trivia topic to determine extracted factual information, and generating trivia questions based on the extracted factual information for presentation to the learner in an interactive game format and corresponding trivia answers; and / or a branched decision-making scenario generated by performing the steps: determining a scenario topic within the course parameters, retrieving and analyzing the course information from the content repository relevant to the scenario topic that suggests decision points, decision choices, and decision consequences, generating the branched decision-making scenario based on at least the decision points, the decision choices, and the decision consequences, and wherein administering the decision-making scenario comprises: recording expert performance data for an expert completing the branched decision-making scenario, recording learner performance data for the learner completing the branched decision-making scenario, comparing the learner performance data against the expert performance data to identify decision-making performance gaps, and recommending by the Al engine resources from the content repository to the learner based on the identified performance gaps.

20. The method of claim 17, wherein the course information is supplemented by externally sourced information relevant to the course objectives by performing the steps: locating and retrieving by the Al engine the externally sourced information; validating the externally sourced information by the Al engine; generating the course content by the Al engine using the course information and the externally sourced information; and displaying an indication of source of the externally sourced information to the user.

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